CN104021502A - Electrical-network load loss risk estimation method suitable for windy and rainy weather - Google Patents

Abstract

The invention discloses an electrical-network load loss risk estimation method suitable for windy and rainy weather, belonging to the technical field of risk estimation of power system. The method comprises the steps that data in the aspects of windy and rainy weather and electrical-network failure tripping is collected, the line failure rate under the windy and rainy weather condition is obtained, and further a power system operation risk estimation model under the windy and rainy weather condition is established; the load-loss probability, the load-loss quantity, the line power and the node voltage under a load loss state are calculated; and different load loss states are combined to obtain the load reduction index, line overload risk value and low-voltage risk value of the power system under the windy and rainy weather condition. The method is especially used for risk estimation when the electrical network loses load under the windy and rainy weather condition, the risk during load loss of the electrical network to be estimated under the windy and rainy weather condition is mastered, risk estimation for the electrical network under the windy and rainy weather condition can be carried out, and safe operation of the electrical network can be ensured.

Description

A kind of electrical network that is applicable under wind and rain weather conditions loses load methods of risk assessment

Technical field

The present invention relates to the risk assessment technology field of electric system, relate in particular to a kind of electrical network mistake load methods of risk assessment under wind and rain weather conditions that is applicable to.

Background technology

Electric system appraisal procedure is studied widely at present.Mainly be divided three classes: determinacy appraisal procedure, probabilistic assessment method, methods of risk assessment.Determinacy appraisal procedure is only paid attention to the most conventionally, the most believable accident; Probabilistic approach does not have the quantification of specific targets in addition to the consequence of accident; Methods of risk assessment has been considered possibility and the consequence seriousness of accident, and the uncertain factor in Operation of Electric Systems is carried out to qualitative assessment, security and the economy of comprehensive coordination electric system.

But the methods of risk assessment aspect electric system mainly completes by following four steps at present: the first step is to obtain Operation of Electric Systems data and state; Second step is to carry out that forecast failure collection is chosen and fault analysis; The 3rd step is that consequence electric system being caused based on forecast failure judges, if occur, power supply risk enters the 4th step, otherwise returns to second step; The 4th step is calculation risk index, according to index, carries out indicating risk.This methods of risk assessment carries out quantifying risk assessment to the uncertain factor in electric system, and carries out risk class division according to the size of risk indicator.But what this methods of risk assessment was chosen is forecast failure collection, there is certain subjectivity and determinacy, the randomness of electric power system fault is considered not enough, make can not to lose load to electrical network under wind and rain weather conditions and carry out risk assessment, can not grasp electrical network comprehensively and lose the risk situation while loading, electrical network mistake load risk can cause the generation of electrical network disastrous accident when more serious.

China Patent Publication No. CN103632310A, open day is on March 12nd, 2014, discloses a kind of large operation of power networks methods of risk assessment, includes following steps: 1) fault and risk factors statistics; 2) risk probability calculates; 3) assessment of the risk quantification based on load loss; 4) the hierarchical and outcome evaluation of risk indicator.Risk assessment when this large operation of power networks methods of risk assessment does not lose load for electrical network under wind and rain weather conditions, can not grasp electrical network under wind and rain weather conditions and lose the risk situation while loading, when electrical network mistake load risk is more serious under wind and rain weather conditions, can cause the generation of electrical network disastrous accident.

Summary of the invention

The present invention is the risk assessment in order to solve the methods of risk assessment of existing electric system aspect and not lose load for electrical network under wind and rain weather conditions, can not grasp electrical network under wind and rain weather conditions and lose the risk situation while loading, the deficiency that easily causes electrical network disastrous accident to occur, a kind of electrical network mistake load methods of risk assessment under wind and rain weather conditions that is applicable to is provided, risk assessment when the method is lost load for electrical network under wind and rain weather conditions specially, grasp evaluated electrical network under wind and rain weather conditions and lose the risk situation while loading, to electrical network is carried out to the risk assessment under wind and rain weather conditions, thereby guarantee the safe operation of electrical network.

To achieve these goals, the present invention is by the following technical solutions:

Be applicable to electrical network under wind and rain weather conditions and lose a load methods of risk assessment, comprise the following steps:

(1-1) obtain the historical record of weather data, track data, generator data and the load data of evaluated electrical network Real-Time Monitoring;

(1-2) set up the element failure rate computation model of evaluated electrical network under wind and rain weather conditions, according to element failure rate computation model, calculate the element failure rate under wind and rain weather conditions;

(1-3) adopt Monte Carlo method to extract the electric power system fault state under wind and rain weather conditions, and carry out power system accident probability calculation according to the electric power system fault state under wind and rain weather conditions and element failure rate, obtain the power system accident probability of evaluated electrical network;

(1-4) under electric power system fault state, carry out optimum and lose load calculating, obtain the optimum load of losing;

(1-5) calculate a series of risk indicators of electric system under wind and rain weather conditions, and according to risk indicator, the electric system under wind and rain weather conditions is carried out to risk assessment, obtain the risk evaluation result of electric system under wind and rain weather conditions;

(1-6) analyze the risk evaluation result of electric system under wind and rain weather conditions, obtain the Operation of Electric Systems control strategy of evaluated electrical network after losing load under wind and rain weather conditions.

Risk assessment when this programme loses load for electrical network under wind and rain weather conditions specially, grasp evaluated electrical network under wind and rain weather conditions and lose the risk situation while loading, to electrical network is carried out to the risk assessment under wind and rain weather conditions, thus the safe operation of assurance electrical network.Under this programme simulation wind and rain weather conditions, uncertainty and the randomness of evaluated operation of power networks, utilize optimum Nonlinear programming Model to electrical network comprehensive assessment.First collect wind and rain weather and electrical network and cause the data that hinder tripping operation aspect, obtain the element failure rate under wind and rain weather conditions, and then set up the Operation of Electric Systems risk evaluation model under wind and rain weather conditions; Then losing calculating mistake Load Probability under load condition, losing load, line power and node voltage; Last comprehensive each loses load condition, the load that draws electric system under wind and rain weather conditions is cut down index, circuit overload value-at-risk and low-voltage value-at-risk, the power supply reliability degree of comprehensive assessment electrical network links, to find the weak link of electric system under wind and rain weather conditions, take specific aim safeguard procedures.

As preferably, weather data comprises wind speed v, the rainfall amount p of climatic region, evaluated electrical network place;

Each circuit that track data comprises evaluated electrical network is at a measurement period hour internal fault frequency n, hour MTTR that stops transport, each circuit unit resistance perunit value r, the per unit reactance x of unit, unit admittance b perunit value, line length l, transmission capacity S over the ground _t;

Generator data are included as the normal hours run MTTF of each generator of evaluated mains supply, hour MTTR that stops transport, the affiliated power plant of generator, capacity P _g, export idle lower limit Q _gminwith upper limit Q _gmax;

Load data comprises the burden with power maximal value P of each transformer station in measurement period hour _dwith load or burden without work maximal value Q _d.

As preferably, the element failure rate λ under wind and rain weather conditions _mcomputing formula be:

${\mathrm{\λ}}_m={\mathrm{\λ}}_m^{\′}\frac{N_m+S_m}{S_m}{F}_m={\mathrm{\λ}}_m^{\′}\frac{\underset{i}{\mathrm{\Σ}}{n}_i+\underset{i}{\mathrm{\Σ}}{s}_i}{\underset{i}{\mathrm{\Σ}}{s}_i}{F}_m---\left(1\right)$

Wherein, λ _mthe element failure rate of element m under wind and rain weather conditions; λ ' _mit is the failure rate of element m long-time running under normal climate condition; N _mit is the expectation continuous service number of days of element m under normal climate condition; S _mit is the expectation continuous service number of days of element m under wind and rain weather conditions; F _mthe fault that is element m occurs in the number percent under wind and rain weather conditions, 0≤F _m≤ 1, first obtain fault and occur in number of times or the number of days under wind and rain weather conditions, then draw as ratio with total degree or total number of days of fault generation; n _iit is the continuous service number of days of element m under the subnormal weather conditions of i; S _iit is the continuous service number of days of element m under the i time wind and rain weather conditions.

As preferably, extract the different electric power system fault state being formed by one or more element faults under wind and rain weather conditions, and the duration of recording each electric power system fault state;

The component population of supposing evaluated grid power system is N _c, the power system accident that element m stoppage in transit causes is arbitrarily designated as E _m, power system accident E _mpower system accident probability P (the E occurring _m) computing formula be:

$P\left(E_m\right)=\frac{{\mathrm{\λ}}_m}{{\mathrm{\λ}}_m+{\mathrm{\μ}}_m}\underset{n\≠m}{\mathrm{\Π}}(1-\frac{{\mathrm{\λ}}_n}{{\mathrm{\λ}}_n+{\mathrm{\μ}}_n})(m=\mathrm{1,2},...,N_c;n=\mathrm{1,2},...,N_c)---\left(2\right),$

Wherein, μ _mthe repair rate of one of them element m under wind and rain weather conditions in component population; μ _nit is wherein another element n in component population repair rate under wind and rain weather conditions; λ _mthe element failure rate of one of them element m under wind and rain weather conditions in component population; λ _nit is wherein another element n in component population element failure rate under wind and rain weather conditions.

As preferably, to the different electric power system fault state under the wind and rain weather conditions that extract, adopt respectively optimum Nonlinear programming Model based on AC power flow to calculate node voltage and the line power of electric system;

Optimum Nonlinear programming Model based on AC power flow is:

$C_i=\min \underset{i=1}{\overset{n}{\mathrm{\Σ}}}(P_{\mathrm{di}}-P_{\mathrm{li}})---\left(3\right),$

$\underset{s.t.j=1}{\mathrm{\Σ}}{U}_i{U}_j(g_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+b_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})+P_{\mathrm{li}}-P_{\mathrm{gi}}=0---\left(4\right),$

$\underset{j=1}{\mathrm{\Σ}}{U}_i{U}_j(g_{\mathrm{ik}}\sin {\mathrm{\θ}}_{\mathrm{ij}}-b_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}})+Q_{\mathrm{li}}-Q_{\mathrm{li}}=.0---\left(5\right),$

0≤P _li≤P _di，0≤Q _li≤Q _di?(6)，

U _imin≤U _i≤U _imax?(7)，

P _gimin≤P _gi≤P _gimax，Q _gimin≤Q _gi≤Q _gimax?(8)，

$P_{\mathrm{ij}}^2+Q_{\mathrm{IJ}}^2\≤S_{\mathrm{ij}\max }^2---\left(9\right),$

$\left\{\begin{array}{c}{P}_{\mathrm{ij}}=U_i{U}_j(g_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+b_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})-U_i^2{g}_{\mathrm{in}}\\ Q_{\mathrm{ij}}=-U_i{U}_j(b_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}-g_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})+U_i^2{b}_{\mathrm{ij}}\end{array}\right.---\left(10\right),$

Wherein, G _iminimum load, the P of losing _dithe active power before node i load is cut down, P _ijthe active power after node i load is cut down, Q _dithe reactive power before node i load is cut down, Q _lithe reactive power after node i load is cut down, U _i, U _jthe voltage of node i, j, θ _ijthe phase angle difference between node i, j, P _gi, P _gimax, P _giminactive power and the bound thereof of node i place generator, Q _gi, Q _gimax, Q _giminreactive power and the bound thereof of node i place generator, S _ijmaxcircuit transmission capacity, P _ijthe active power of branch road ij, Q _ijthe reactive power of branch road ij, U _imax, U _iminthe bound of the voltage of node i, p _ijthe line admittance between node i and j, g _ijit is the line conductance between node i and j;

Formula (3) is the objective function of optimal programming, represents the minimum load of losing; Formula (4) is AC power flow equation, represents the active power constraint of power balance equation; Formula (5) is AC power flow equation, represents the reactive power constraint of power balance equation; Formula (6) is lost the bound constraint of load afterload node active power and reactive power; Formula (7) is the voltage bound constraint of node; Formula (8) is generated power and idle bound constraint; Formula (9) is the meritorious idle not out-of-limit constraint of circuit; Formula (10) is that circuit is meritorious and idle;

For improving the counting yield of optimum Nonlinear programming Model, first use formula (4), (5) to calculate node voltage and the line power of electric system; If circuit nonoverload and trend convergence, do not lose load and calculate; If circuit overload and trend do not restrain, carry out optimum Nonlinear programming Model calculating, thereby obtain the optimum mistake load of evaluated electrical network.

As preferably, under wind and rain weather conditions, a series of risk indicators of electric system comprise:

(6-1) lose Load Probability PLC index, the duration t of each electric power system fault state _isubstitution formula (11) carries out electric system and loses Load Probability assessment, obtains and loses Load Probability PLC index: the computing formula of losing Load Probability PLC index is:

$\mathrm{PLC}=\underset{i\∈s}{\mathrm{\Σ}}\frac{t_i}{T}---\left(11\right),$

In formula (11), S is the state set that load is lost in electric system; T is total simulation hour; By i, lose duration of load application and be added the T.T. that obtains losing load, this T.T. is exactly to lose duration of load application to account for the ratio of T.T. with the ratio of simulation T.T., loses Load Probability;

(6-2) lose load of machinery systems ELC index, each electric power system fault under wind and rain weather conditions is lost to the minimum of load condition and lose load C _isubstitution formula (12), obtains the mistake load of machinery systems ELC index of quantitatively estimating electric system under wind and rain weather conditions in total simulation hour, and the computing formula of losing load of machinery systems ELC index is:

$\mathrm{ELC}=\frac{9760}{T}\underset{i\∈s}{\mathrm{\Σ}}{C}_i---\left(12\right),$

This mistake load of machinery systems ELC index is mistake load number conversion total in total simulation hour and becomes the mistake load value in total simulation hour;

(6-3) power supply abundant intensity EENS index, the duration t of each electric power system fault state _ilose load C with each minimum of losing load condition _isubstitution formula (13), obtains the power supply abundant intensity EENS index of electric system under quantitative estimation wind and rain weather conditions, and the computing formula of power supply abundant intensity EENS index is:

$\mathrm{EENS}=8760\underset{I\∈s}{\mathrm{\Σ}}{C}_i\frac{t_i}{T}---\left(13\right),$

C in formula (13) _ibe i the mistake load of losing load condition, be multiplied by the scarce generated energy that obtains for lasting hour of each mistake load;

(6-4) circuit overload risk R _oLmindex, power system accident probability P (E _m) and line power P _ijsubstitution formula (14) is tried to achieve circuit overload risk R _oLmindex, circuit overload risk R _oLmthe computing formula of index is:

R in formula (14) _oLmit is the overload risk of circuit after element m fault under wind and rain weather conditions is stopped transport; G ₁it is electric system overload accident condition collection under wind and rain weather conditions; K is circuit sum; P _ijit is the trend in circuit under wind and rain weather conditions; P ' _ijit is circuit active power capacity;

(6-5) node low-voltage risk R _lVmindex, power system accident probability P (Em) and node voltage V _isubstitution formula (15) is tried to achieve node low-voltage risk R _lVmindex, node low-voltage risk R _lVmthe computing formula of index is:

$R_{\mathrm{LVm}}=\underset{E_m\∈G_2}{\mathrm{\Σ}}P\left(E_M\right)\×\underset{I=1}{\overset{n}{\mathrm{\Σ}}}(e^{V_0-V_i}-1){,V}_0-V_i>0---\left(15\right),$

In formula (15), R _lVmit is the node low-voltage risk after element m fault under wind and rain weather conditions is stopped transport; G ₂it is electric system node low-voltage accident condition collection under wind and rain weather conditions; N is node sum; V _iactual node voltage during line fault under wind and rain weather conditions; V ₀the node voltage of circuit while normally moving.

Lose the possibility that Load Probability PLC has assessed electric system generation mistake load under wind and rain weather conditions, the standard based on amount, the probability that loses the generation of loading than qualitative analysis is more directly perceived.Power supply abundant intensity EENS index evaluation when under wind and rain weather conditions there is various fault in electric system, lack every year on average the electric weight of confession, can quantitatively try to achieve the power supply capacity of assessed electric system.Circuit overload risk R _oLmassessed the circuit overload risk that under wind and rain weather conditions, electric system is caused by various faults, thereby obtain the caused circuit overload value-at-risk of each line fault, generally in 10-5～10-4 rank, by this index, can accurately be located under wind and rain weather conditions when electric system is broken down and be had the larger circuit of overload risk, for operation, department provides reference frame.Node low-voltage risk R _lVmassessed the node low-voltage risk that under wind and rain weather conditions, electric system is caused by various faults, calculate the caused node low-voltage of each line fault value-at-risk, generally also in 10-5～10-4 rank, when electric system is broken down, by this index, can conveniently locate and under wind and rain weather conditions, have the node that low-voltage risk is larger.The every data that obtain according to risk assessment process are carried out risk assessment to the electric system under wind and rain weather conditions, obtain electrical network under wind and rain weather conditions and lose the corresponding operation control strategy of load risk assessment.

As preferably, the optimum that optimum Nonlinear programming Model adopts the nonlinear solver (fmincon) based on matlab optimization tool bag to calculate evaluated electrical network loses load, and the calculating implementation procedure that this optimum loses load is:

(7-1) select fmincon to process the form X=fmincon (FUN, XO, A, B, Aeq, Beq, LB, UB, NONLCON) of nonlinear programming problem;

(7-2) set unknown vector X, the subvector using the unknown quantity in optimum Nonlinear programming Model as X, and compose with initial value;

(7-3) set the bound of unknown vector X;

(7-4) write and take minimum and lose the subfunction FUN that load is objective function;

(7-5) write optimum Nonlinear programming Model and process subfunction NONLCON, the value of the vectorial X that each iteration is obtained is carried out iterative;

(7-6) fmincon solves the minimum load of losing after calling subfunction FUN, NONLCON, and this minimum is lost the optimum mistake load that load is evaluated electrical network under wind and rain weather conditions.

The present invention can reach following effect:

1, first the present invention collects the data that wind and rain weather and electrical network cause barrier tripping operation aspect, obtains the element failure rate under wind and rain weather conditions, and then sets up the Operation of Electric Systems risk evaluation model under wind and rain weather conditions; Then losing calculating mistake Load Probability under load condition, losing load, line power and node voltage; Last comprehensive each loses load condition, the load that draws electric system under wind and rain weather conditions is cut down index, circuit overload value-at-risk and low-voltage value-at-risk, the power supply reliability degree of comprehensive assessment electrical network links, to find the weak link of electric system under wind and rain weather conditions, take specific aim safeguard procedures.

2, the present invention is directed to electrical network under wind and rain weather conditions and lose the risk assessment while loading, grasp evaluated electrical network under wind and rain weather conditions and lose the risk situation while loading, to electrical network is carried out to the risk assessment under wind and rain weather conditions, thus the safe operation of assurance electrical network.

3, the present invention simulates uncertainty and the randomness of evaluated operation of power networks under wind and rain weather conditions, utilizes optimum Nonlinear programming Model to electrical network comprehensive assessment.

Accompanying drawing explanation

Fig. 1 is a kind of schematic process flow diagram of the present invention.

Fig. 2 is that a kind of AC power flow of the present invention and DC power flow calculate relatively schematic diagram of mistake Load Probability result.

Fig. 3 is that a kind of AC power flow of the present invention and DC power flow calculate the annual relatively schematic diagram of load result that loses.

Fig. 4 is that a kind of AC power flow of the present invention and DC power flow calculate relatively schematic diagram of expected loss of energy result.

Fig. 5 is a kind of stoppage in transit circuit of the present invention and the large logotype of systematic failures probability.

Fig. 6 is a kind of stoppage in transit circuit of the present invention and the system overload logotype that has a big risk.

Fig. 7 is a kind of stoppage in transit circuit of the present invention and the node low-voltage logotype that has a big risk.

Embodiment

Below by embodiment, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.

Embodiment: a kind of electrical network that is applicable under wind and rain weather conditions loses load methods of risk assessment, shown in Figure 1, comprises the following steps:

(1-1) obtain the historical record of weather data, track data, generator data and the load data of evaluated electrical network Real-Time Monitoring; Weather data comprises wind speed v, the rainfall amount p of climatic region, evaluated electrical network place; Each circuit that track data comprises evaluated electrical network is at a measurement period hour internal fault frequency n, hour MTTR that stops transport, each circuit unit resistance perunit value r, the per unit reactance x of unit, unit admittance b perunit value, line length l, transmission capacity S over the ground _l; Generator data are included as the normal hours run MTTF of each generator of evaluated mains supply, hour MTTR that stops transport, the affiliated power plant of generator, capacity P _g, export idle lower limit Q _gminwith upper limit Q _gmax; Load data comprises the burden with power maximal value P of each transformer station in measurement period hour _dwith load or burden without work maximal value Q _d.

(1-2) set up the element failure rate computation model of evaluated electrical network under wind and rain weather conditions, according to element failure rate computation model, calculate the element failure rate under wind and rain weather conditions; Element failure rate λ under wind and rain weather conditions _mcomputing formula be:

${\mathrm{\λ}}_m={\mathrm{\λ}}_m^{\′}\frac{N_m+S_m}{S_m}{F}_m={\mathrm{\λ}}_m^{\′}\frac{\underset{i}{\mathrm{\Σ}}{n}_i+\underset{i}{\mathrm{\Σ}}{s}_i}{\underset{i}{\mathrm{\Σ}}{s}_i}{F}_m---\left(1\right)$

Wherein, λ _mthe element failure rate of element m under wind and rain weather conditions; λ ' _mit is the failure rate of element m long-time running under normal climate condition; N _mit is the expectation continuous service number of days of element m under normal climate condition; S _mit is the expectation continuous service number of days of element m under wind and rain weather conditions; F _mthe fault that is element m occurs in the number percent under wind and rain weather conditions, 0≤F _m≤ 1, first obtain fault and occur in number of times or the number of days under wind and rain weather conditions, then draw as ratio with total degree or total number of days of fault generation; n _iit is the continuous service number of days of element m under the subnormal weather conditions of i; S _iit is the continuous service number of days of element m under the i time wind and rain weather conditions.

(1-3) adopt Monte Carlo method to extract the electric power system fault state under wind and rain weather conditions, and carry out power system accident probability calculation according to the electric power system fault state under wind and rain weather conditions and element failure rate, obtain the power system accident probability of evaluated electrical network; Extract the different electric power system fault state being formed by one or more element faults under wind and rain weather conditions, and the duration of recording each electric power system fault state;

The component population of supposing evaluated grid power system is N _c, the power system accident that element m stoppage in transit causes is arbitrarily designated as E _m, power system accident E _mpower system accident probability P (the E occurring _m) computing formula be:

$P\left(E_m\right)=\frac{{\mathrm{\λ}}_m}{{\mathrm{\λ}}_m+{\mathrm{\μ}}_m}\underset{n\≠m}{\mathrm{\Π}}(1-\frac{{\mathrm{\λ}}_n}{{\mathrm{\λ}}_n+{\mathrm{\μ}}_n})(m=\mathrm{1,2},...,N_c;n=\mathrm{1,2},...,N_c)---\left(2\right),$

Wherein, μ _mthe repair rate of one of them element m under wind and rain weather conditions in component population; μ _nit is wherein another element n in component population repair rate under wind and rain weather conditions; λ _mthe element failure rate of one of them element m under wind and rain weather conditions in component population; λ _nit is wherein another element n in component population element failure rate under wind and rain weather conditions.

(1-4) under electric power system fault state, carry out optimum and lose load calculating, obtain the optimum load of losing; To the different electric power system fault state under the wind and rain weather conditions that extract, adopt respectively optimum Nonlinear programming Model based on AC power flow to calculate node voltage and the line power of electric system;

Optimum Nonlinear programming Model based on AC power flow is:

$C_i=\min \underset{i=1}{\overset{n}{\mathrm{\Σ}}}(P_{\mathrm{di}}-P_{\mathrm{li}})---\left(3\right),$

$\underset{s.t.j=1}{\mathrm{\Σ}}{U}_i{U}_j(g_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+b_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})+P_{\mathrm{li}}-P_{\mathrm{gi}}=0---\left(4\right),$

$\underset{j=1}{\mathrm{\Σ}}{U}_i{U}_j(g_{\mathrm{ik}}\sin {\mathrm{\θ}}_{\mathrm{ij}}-b_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}})+Q_{\mathrm{li}}-Q_{\mathrm{li}}=.0---\left(5\right),$

0≤P _li≤P _di，0≤Q _li≤Q _di?(6)，

U _imAx≤U _i≤U _imax?(7)，

P _gimin≤P _gi≤P _gimax，Q _gimin≤Q _gi≤Q _gimax?(8)，

$P_{\mathrm{ij}}^2+Q_{\mathrm{IJ}}^2\≤S_{\mathrm{ij}\max }^2---\left(9\right),$

$\left\{\begin{array}{c}{P}_{\mathrm{ij}}=U_i{U}_j(g_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+b_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})-U_i^2{g}_{\mathrm{in}}\\ Q_{\mathrm{ij}}=-U_i{U}_j(b_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}-g_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})+U_i^2{b}_{\mathrm{ij}}\end{array}\right.---\left(10\right),$

Wherein, C _iminimum load, the P of losing _dithe active power before node i load is cut down, P _lithe active power after node i load is cut down, Q _dithe reactive power before node i load is cut down, Q _lithe reactive power after node i load is cut down, U _i, U _jthe voltage of node i, j, θ _ijthe phase angle difference between node i, j, P _gi, P _gimax, P _giminactive power and the bound thereof of node i place generator, Q _gi, Q _gimax, Q _giminreactive power and the bound thereof of node i place generator, S _ijmaxcircuit transmission capacity, P _ijthe active power of branch road ij, Q _ijthe reactive power of branch road i j, U _imax, U _iminthe bound of the voltage of node i, b _ijthe line admittance between node i and j, g _ijit is the line conductance between node i and j;

Formula (3) is the objective function of optimal programming, represents the minimum load of losing; Formula (4) is AC power flow equation, represents the active power constraint of power balance equation; Formula (5) is AC power flow equation, represents the reactive power constraint of power balance equation; Formula (6) is lost the bound constraint of load afterload node active power and reactive power; Formula (7) is the voltage bound constraint of node; Formula (8) is generated power and idle bound constraint; Formula (9) is the meritorious idle not out-of-limit constraint of circuit; Formula (10) is that circuit is meritorious and idle;

For improving the counting yield of optimum Nonlinear programming Model, first use formula (4), (5) to calculate node voltage and the line power of electric system; If circuit nonoverload and trend convergence, do not lose load and calculate; If circuit overload and trend do not restrain, carry out optimum Nonlinear programming Model calculating, thereby obtain the optimum mistake load of evaluated electrical network.

The optimum that optimum Nonlinear programming Model adopts the nonlinear solver (fmincon) based on matlab optimization tool bag to calculate evaluated electrical network loses load, and the calculating implementation procedure that this optimum loses load is:

(7-1) select fmincon to process the form of nonlinear programming problem

X＝fmincon(FUN，XO，A，B，Aeq，Beq，LB，UB，NONLCON)；

(7-2) set unknown vector X, the subvector using the unknown quantity in optimum Nonlinear programming Model as X, and compose with initial value;

(7-3) set the bound of unknown vector X;

(7-4) write and take minimum and lose the subfunction FUN that load is objective function;

(7-5) write optimum Nonlinear programming Model and process subfunction NONLCON, the value of the vectorial X that each iteration is obtained is carried out iterative;

(7-6) fmincon solves the minimum load of losing after calling subfunction FUN, NONLCON, and this minimum is lost the optimum mistake load that load is evaluated electrical network under wind and rain weather conditions.

(1-5) calculate a series of risk indicators of electric system under wind and rain weather conditions, and according to risk indicator, the electric system under wind and rain weather conditions is carried out to risk assessment, obtain the risk evaluation result of electric system under wind and rain weather conditions; Under wind and rain weather conditions, a series of risk indicators of electric system comprise:

(6-1) lose Load Probability PLC index, the duration t of each electric power system fault state _isubstitution formula (11) carries out electric system and loses Load Probability assessment, obtains and loses Load Probability PLC index; The computing formula of losing Load Probability PLC index is:

$\mathrm{PLC}=\underset{i\∈s}{\mathrm{\Σ}}\frac{t_i}{T}---\left(11\right),$

In formula (11), S is the state set that load is lost in electric system; T is total simulation hour; By i, lose duration of load application and be added the T.T. that obtains losing load, this T.T. is exactly to lose duration of load application to account for the ratio of T.T. with the ratio of simulation T.T., loses Load Probability;

(6-2) lose load of machinery systems ELC index, each electric power system fault under wind and rain weather conditions is lost to the minimum of load condition and lose load C _isubstitution formula (12), obtains the mistake load of machinery systems ELC index of quantitatively estimating electric system under wind and rain weather conditions in total simulation hour, and the computing formula of losing load of machinery systems ELC index is:

$\mathrm{ELC}=\frac{9760}{T}\underset{i\∈s}{\mathrm{\Σ}}{C}_i---\left(12\right),$

This mistake load of machinery systems ELC index is mistake load number conversion total in total simulation hour and becomes the mistake load value in total simulation hour;

(6-3) power supply abundant intensity EENS index, the duration t of each electric power system fault state _ilose load C with each minimum of losing load condition _isubstitution formula (13), obtains the power supply abundant intensity EENS index of electric system under quantitative estimation wind and rain weather conditions, and the computing formula of power supply abundant intensity EENS index is:

$\mathrm{EENS}=8760\underset{I\∈s}{\mathrm{\Σ}}{C}_i\frac{t_i}{T}---\left(13\right),$

C in formula (13) _ibe i the mistake load of losing load condition, be multiplied by the scarce generated energy that obtains for lasting hour of each mistake load;

(6-4) circuit overload risk R _oLmindex, power system accident probability P (Em) and line power P _ijsubstitution formula (14) is tried to achieve circuit overload risk R _oLmindex, circuit overload risk R _oLmthe computing formula of index is:

R in formula (14) _oLmit is the overload risk of circuit after element m fault under wind and rain weather conditions is stopped transport; G ₁it is electric system overload accident condition collection under wind and rain weather conditions; K is circuit sum; P _ijit is the trend in circuit under wind and rain weather conditions; P ' _ijit is circuit active power capacity;

(6-5) node low-voltage risk R _lVmindex, power system accident probability P (Em) and node voltage V _isubstitution formula (15) is tried to achieve node low-voltage risk R _lVmindex, node low-voltage risk R _lVmthe computing formula of index is:

$R_{\mathrm{LVm}}=\underset{E_m\∈G_2}{\mathrm{\Σ}}P\left(E_M\right)\×\underset{I=1}{\overset{n}{\mathrm{\Σ}}}(e^{V_0-V_i}-1){,V}_0-V_i>0---\left(15\right),$

In formula (15), R _lVmit is the node low-voltage risk after element m fault under wind and rain weather conditions is stopped transport; G ₂it is electric system node low-voltage accident condition collection under wind and rain weather conditions; N is node sum; V _iactual node voltage during line fault under wind and rain weather conditions; V ₀the node voltage of circuit while normally moving.

(1-6) analyze the risk evaluation result of electric system under wind and rain weather conditions, obtain the Operation of Electric Systems control strategy of evaluated electrical network after losing load under wind and rain weather conditions.

Risk assessment when this example loses load for electrical network under wind and rain weather conditions specially, grasp evaluated electrical network under wind and rain weather conditions and lose the risk situation while loading, to electrical network is carried out to the risk assessment under wind and rain weather conditions, thus the safe operation of assurance electrical network.Uncertainty and the randomness of evaluated operation of power networks under this real case simulation wind and rain weather conditions, utilize optimum Nonlinear programming Model to electrical network comprehensive assessment.First collect wind and rain weather and electrical network and cause the data that hinder tripping operation aspect, obtain the element failure rate under wind and rain weather conditions, and then set up the Operation of Electric Systems risk evaluation model under wind and rain weather conditions; Then losing calculating mistake Load Probability under load condition, losing load, line power and node voltage; Last comprehensive each loses load condition, the load that draws electric system under wind and rain weather conditions is cut down index, circuit overload value-at-risk and low-voltage value-at-risk, the power supply reliability degree of comprehensive assessment electrical network links, to find the weak link of electric system under wind and rain weather conditions, take specific aim safeguard procedures.Lose the possibility that Load Probability PLC has assessed electric system generation mistake load under wind and rain weather conditions, the standard based on amount, the probability that loses the generation of loading than qualitative analysis is more directly perceived.Power supply abundant intensity EENS index evaluation when under wind and rain weather conditions there is various fault in electric system, lack every year on average the electric weight of confession, can quantitatively try to achieve the power supply capacity of assessed electric system.Circuit overload risk R _oLmassessed the circuit overload risk that under wind and rain weather conditions, electric system is caused by various faults, thereby obtain the caused circuit overload value-at-risk of each line fault, generally in 10-5～10-4 rank, by this index, can accurately be located under wind and rain weather conditions when electric system is broken down and be had the larger circuit of overload risk, for operation, department provides reference frame.Node low-voltage risk R _lVmassessed the node low-voltage risk that under wind and rain weather conditions, electric system is caused by various faults, calculate the caused node low-voltage of each line fault value-at-risk, generally also in 10-5～10-4 rank, when electric system is broken down, by this index, can conveniently locate and under wind and rain weather conditions, have the node that low-voltage risk is larger.The every data that obtain according to risk assessment process are carried out risk assessment to the electric system under wind and rain weather conditions, obtain electrical network under wind and rain weather conditions and lose the corresponding operation control strategy of load risk assessment.

Implementation step 1: obtain measured data.Collect climatic data, track data, generator data and the load data of evaluated electrical network, concrete measured data is as shown in table 1.The i time wind speed v is greater than 14m/s or rainfall p and is greater than the lasting number of days of 25mm and counts S _i, element m is S in 5 years _isummation obtains S _m.According to n and the n of statistics _stry to achieve ratio F _m.The present invention is using IEEE-RTS79 reliability test system as realizing example.

Risk assessment measured data under table 1 wind and rain weather conditions

Implementation step 2: calculate the element failure rate under wind and rain weather conditions.Foundation calculates the element failure rate under wind and rain weather conditions suc as formula the computing formula of the element failure rate under the wind and rain weather conditions shown in (1).

Implementation step 3: the power system accident probability that calculates evaluated electrical network.Binding member failure rate extracts the electric power system fault state under a large amount of wind and rain weather conditions, and calculates power system accident probability according to formula (2).

Implementation step 4: calculate the optimum load of losing.This example is picked out 10 faulty lines of power system accident maximum probability under wind and rain weather, as shown in table 2:

Power system accident probability (10 under table 2 wind and rain weather conditions ^-3)

Accident circuit number	27	21	9	23	36	35	4
								Accident probability	2.26	1.97	1.93	1.85	1.85	1.27	1.05
Accident circuit number	5	31	10	?	?	?	?
								Accident probability	0.78	0.76	0.69	?	?	?	?

On the basis of implementation step 3, judge successively whether each electric power system fault state has trend not restrain or the out-of-limit risk of line power, if having, utilize AC power flow and optimum Nonlinear programming Model to calculate the optimum load of losing.

Implementation step 5: calculate a series of risk assessment indexs.On the basis of implementation step 3,4, calculate and lose load risk, circuit overload risk and low-voltage value-at-risk, result is as shown in table 3, table 4, table 5.

Under table 3 wind and rain weather conditions, lose load risk indicator result of calculation

The larger circuit (10 of circuit on power system overload risk under table 4 wind and rain weather conditions ^-5)

Accident circuit number	31	18	12	38	11
						Overload value-at-risk	40.7	7.72	0.0068	3.3E-5	2.0E-5

The larger circuit (10 of electric system low-voltage value-at-risk under table 5 wind and rain weather conditions ^-5)

Accident circuit number	36	25	10	32	29	37	4
								Low-voltage value-at-risk	107	9.9	5.3	3.0	2.9	2.9	1.7
Accident circuit number	6	8	18	?	?	?	?
								Low-voltage value-at-risk	1.1	0.64	0.62	?	?	?	?

Table 3 is mistake load risk indicators of moving in IEEE-RTS79 system, and it has shown that the AC method of the present invention's use is more superior than DC-method.As can be seen from the table, DC-method and AC method result of calculation are variant, and this sets up in theory, and DC-method gained index and direct current standard exist the deviation of 5% left and right, in acceptable allowed band.

Lose as can be seen from Figure 2 the result that load PLC index calculates by DC-method and AC method all slightly little than standard results, the deviation of AC method and standard is larger relatively in short-term for simulated time, and with the growth of simulated time, convergent tendency is that AC method is better than DC-method, and AC method deviation is less than DC-method deviation, illustrate that PLC index comparison of computational results of the present invention is desirable.

Lose every year on average as can be seen from Figure 3 load ELC index slightly larger than DC-method criterion calculation result by DC-method result of calculation, along with simulated time increases, deviation is increase tendency slightly.The deviation of AC method result of calculation of the present invention and AC method standard is less than the deviation of DC-method, and along with simulated time increases, convergent tendency is consistent with AC method standard, illustrates that ELC index comparison of computational results of the present invention is desirable.

Expected loss of energy EENS index is obviously better than DC-method by AC method result of calculation every year on average as can be seen from Figure 4, simulated time length no matter, AC method and standard deviation are less, and convergent tendency is consistent with standard, illustrate that EENS index comparison of computational results of the present invention is desirable.

The present invention draws risk indicator by AC method, and table 4, table 5 have been listed the larger faulty line of value-at-risk separately.

Implementation step 6: analyze risk evaluation result, obtain moving control strategy.Table 2, table 4 and table 5 have reflected under wind and rain weather influence after different line fault that electric system risk has complicacy: when 1) after line tripping, the accident probability of system is large, system risk is not necessarily large, as the 21st, 23,27, No. 35 circuits; 2) after line tripping, the accident probability of a system hour overload has a big risk, as the 12nd, 18, No. 38 circuits; 3) when after line tripping, overload has a big risk, low-voltage risk is little, as the 12nd, 31, No. 38 circuits; 4) when after line tripping, the accident probability of system is large, overload has a big risk, as No. 31 circuit; 5) when after line tripping, the accident probability of system is large, low-voltage has a big risk, as the 4th, 10, No. 36 circuits.

In order more clearly to analyze Study of Risk Evaluation Analysis for Power System result of calculation under wind and rain weather conditions, make a concrete analysis of accident probability, overload risk and the low-voltage risk of electric system below.

Fig. 5 has listed 10 circuits of systematic failures maximum probability after line tripping, circuit sequence is: 27>21>9>23=36G reatT.GreaT.GT35>4>5GreatT.G reaT.GT31>10, and after these line fault tripping operations, systematic failures probable value is between 0.0005～0.0025; Fig. 6 has listed to stop transport and has caused 5 circuits that system overload value-at-risk is larger, circuit sequence is: 31>18>12>38Gre atT.GreaT.GT11, and after these line fault tripping operations, overload value-at-risk is between 0.000002～0.00045; Fig. 7 has listed to stop transport and has caused 10 circuits of low-voltage risk maximum, circuit sequence is: 36 > 25 > 10 > 32 > 29=37 > 4 > 6 > 8 > 18, after these line fault tripping operations, low-voltage value-at-risk is between 0.00002～0.0012.

From above analysis, find, after No. 31 line fault is stopped transport, system overload value-at-risk is larger, and systematic failures probability is relatively large, and the risk of line power transmission overload is relatively large; 4th, after 10, No. 36 line faults stoppages in transit, systematic failures probability is relatively large, and low-voltage risk is relatively large.No. 31 connection node 17 and node 22, have generator access on node 22, node 17 is connected nodes.No. 31 line failure rates that obtain from IEEE-RTS79 system are 0.54 times/year (in Table 6).

Table 6 line failure rate detail list

From table 6, can find out, No. 31 circuits are circuits for failure rate maximum, when its tripping operation, stop transport, and power transfer, to All other routes, easily causes circuit overload; 4th, after 10, No. 36 line trippings, system low-voltage is very risky, need to lose a large amount of loads and just can make system voltage return to tolerance interval.These 5 weak links that transmission line of electricity is power transmission network, need pay close attention to.

After stoppage in transit, cause relatively large the 18th, 12, No. 38 circuits in addition of circuit overload risk; After stoppage in transit, cause relatively large the 25th, 32,29,37,4, No. 6 circuits in addition of low-voltage risk, will strengthen the monitoring administration to these circuits at ordinary times.For line tripping fault, strengthen to guard against must take effective measures: 1) strengthen the maintenance to dangerous branch road, strengthen the relay protection to transmission line of electricity, reduce the probability that broken string occurs dangerous branch road; 2) near the dangerous branch road breaking down, reduce appropriate load, so that system low-voltage value-at-risk returns in normal range, can normally not move and produce large impact system.

Described by reference to the accompanying drawings embodiments of the present invention above, but not limited by above-described embodiment while realizing, those of ordinary skills can make a variety of changes within the scope of the appended claims or revise.

Claims (7)

1. be applicable to electrical network under wind and rain weather conditions and lose a load methods of risk assessment, it is characterized in that, comprise the following steps:

(1-1) obtain the historical record of weather data, track data, generator data and the load data of evaluated electrical network Real-Time Monitoring;

(1-2) set up the element failure rate computation model of evaluated electrical network under wind and rain weather conditions, according to element failure rate computation model, calculate the element failure rate under wind and rain weather conditions;

(1-3) adopt Monte Carlo method to extract the electric power system fault state under wind and rain weather conditions, and carry out power system accident probability calculation according to the electric power system fault state under wind and rain weather conditions and element failure rate, obtain the power system accident probability of evaluated electrical network;

(1-4) under electric power system fault state, carry out optimum and lose load calculating, obtain the optimum load of losing;

(1-5) calculate a series of risk indicators of electric system under wind and rain weather conditions, and according to risk indicator, the electric system under wind and rain weather conditions is carried out to risk assessment, obtain the risk evaluation result of electric system under wind and rain weather conditions;

(1-6) analyze the risk evaluation result of electric system under wind and rain weather conditions, obtain the Operation of Electric Systems control strategy of evaluated electrical network after losing load under wind and rain weather conditions.

2. a kind of electrical network mistake load methods of risk assessment under wind and rain weather conditions that is applicable to according to claim 1, is characterized in that,

Weather data comprises wind speed v, the rainfall amount p of climatic region, evaluated electrical network place;

Each circuit that track data comprises evaluated electrical network is at a measurement period hour internal fault frequency n, hour MTTR that stops transport, each circuit unit resistance perunit value r, the per unit reactance x of unit, unit admittance b perunit value, line length l, transmission capacity S over the ground _l;

Generator data are included as the normal hours run MTTF of each generator of evaluated mains supply, hour MTTR that stops transport, the affiliated power plant of generator, capacity P _g, export idle lower limit Q _gminwith upper limit Q _gmax;

Load data comprises the burden with power maximal value P of each transformer station in measurement period hour _dwith load or burden without work maximal value Q _d.

3. a kind of electrical network mistake load methods of risk assessment under wind and rain weather conditions that is applicable to according to claim 2, is characterized in that the element failure rate λ under wind and rain weather conditions _mcomputing formula be:

Wherein, λ _mthe element failure rate of element m under wind and rain weather conditions; λ _mit is the failure rate of element m long-time running under normal climate condition; N _mit is the expectation continuous service number of days of element m under normal climate condition; S _mit is the expectation continuous service number of days of element m under wind and rain weather conditions; F _mthe fault that is element m occurs in the number percent under wind and rain weather conditions, 0≤F _m≤ 1, first obtain fault and occur in number of times or the number of days under wind and rain weather conditions, then draw as ratio with total degree or total number of days of fault generation; n _iit is the continuous service number of days of element m under the subnormal weather conditions of i; S _iit is the continuous service number of days of element m under the i time wind and rain weather conditions.

4. a kind of electrical network that is applicable under wind and rain weather conditions according to claim 3 loses load methods of risk assessment, it is characterized in that, extract the different electric power system fault state being formed by one or more element faults under wind and rain weather conditions, and the duration of recording each electric power system fault state;

Suppose evaluated grid power system component population be N _c, the power system accident that element m stoppage in transit causes is arbitrarily designated as E _m, power system accident E _mpower system accident probability P (the E occurring _m) computing formula be:

Wherein, μ _mthe repair rate of one of them element m under wind and rain weather conditions in component population; μ _nit is wherein another element n in component population repair rate under wind and rain weather conditions; λ _mthe element failure rate of one of them element m under wind and rain weather conditions in component population; λ _nit is wherein another element n in component population element failure rate under wind and rain weather conditions.

5. a kind of electrical network that is applicable under wind and rain weather conditions according to claim 4 loses load methods of risk assessment, it is characterized in that, to the different electric power system fault state under the wind and rain weather conditions that extract, adopt respectively optimum Nonlinear programming Model based on AC power flow to calculate node voltage and the line power of electric system;

Optimum Nonlinear programming Model based on AC power flow is:

0≤P _li≤P _di，0≤Q _li≤Q _di?(6)，

U _immin≤U _i≤U _imax?(7)，

P _gimin≤P _gi≤P _gimax，Q _gimin≤Q _gi≤Q _gimax?(8)，

Wherein, C _iminimum load, the P of losing _dithe active power before node i load is cut down, P _lithe active power after node i load is cut down, Q _dithe reactive power before node i load is cut down, Q _lithe reactive power after node i load is cut down, U _i, U _jthe voltage of node i, j, θ _ijthe phase angle difference between node i, j, P _gi, P _gimax, P _giminactive power and the bound thereof of node i place generator, Q _gi, Q _gimax, Q _giminreactive power and the bound thereof of node i place generator, S _ijmaxcircuit transmission capacity, P _ijthe active power of branch road i j, Q _ijthe reactive power of branch road i j, U _imax, U _iminthe bound of the voltage of node i, b _ijthe line admittance between node i and j, g _ijit is the line conductance between node i and j;

Formula (3) is the objective function of optimal programming, represents the minimum load of losing; Formula (4) is AC power flow equation, represents the active power constraint of power balance equation; Formula (5) is AC power flow equation, represents the reactive power constraint of power balance equation; Formula (6) is lost the bound constraint of load afterload node active power and reactive power; Formula (7) is the voltage bound constraint of node; Formula (8) is generated power and idle bound constraint; Formula (9) is the meritorious idle not out-of-limit constraint of circuit; Formula (10) is that circuit is meritorious and idle;

For improving the counting yield of optimum Nonlinear programming Model, first use formula (4), (5) to calculate node voltage and the line power of electric system: if circuit nonoverload and trend convergence are not lost load and calculated; If circuit overload and trend do not restrain, carry out optimum Nonlinear programming Model calculating, thereby obtain the optimum mistake load of evaluated electrical network.

6. a kind of electrical network mistake load methods of risk assessment under wind and rain weather conditions that is applicable to according to claim 5, is characterized in that, under wind and rain weather conditions, a series of risk indicators of electric system comprise:

(6-1) lose Load Probability PLC index, the duration t of each electric power system fault state _isubstitution formula (11) carries out electric system and loses Load Probability assessment, obtains and loses Load Probability PLC index; The computing formula of losing Load Probability PLC index is:

In formula (11), S is the state set that load is lost in electric system; T is total simulation hour; By i, lose duration of load application and be added the T.T. that obtains losing load, this T.T. is exactly to lose duration of load application to account for the ratio of T.T. with the ratio of simulation T.T., loses Load Probability;

(6-2) lose load of machinery systems ELC index, each electric power system fault under wind and rain weather conditions is lost to the minimum of load condition and lose load C _isubstitution formula (12), obtains the mistake load of machinery systems ELC index of quantitatively estimating electric system under wind and rain weather conditions in total simulation hour, and the computing formula of losing load of machinery systems ELC index is:

This mistake load of machinery systems ELC index is mistake load number conversion total in total simulation hour and becomes the mistake load value in total simulation hour;

(6-3) power supply abundant intensity EENS index, the duration t of each electric power system fault state _ilose load C with each minimum of losing load condition _isubstitution formula (13), obtains the power supply abundant intensity EENS index of electric system under quantitative estimation wind and rain weather conditions, and the computing formula of power supply abundant intensity EENS index is:

C in formula (13) _ibe i the mistake load of losing load condition, be multiplied by the scarce generated energy that obtains for lasting hour of each mistake load;

(6-4) circuit overload risk R _oLmindex, power system accident probability P (E _m) and line power P _ijsubstitution formula (14) is tried to achieve circuit overload risk R _oLmindex, circuit overload risk R _oLmthe computing formula of index is:

R in formula (14) _oLmit is the overload risk of circuit after element m fault under wind and rain weather conditions is stopped transport; G ₁it is electric system overload accident condition collection under wind and rain weather conditions; K is circuit sum; P _ijit is the trend in circuit under wind and rain weather conditions; P ' _ijit is circuit active power capacity;

(6-5) node low-voltage risk R _lVntindex, power system accident probability P (Em) and node voltage V _isubstitution formula (15) is tried to achieve node low-voltage risk R _lVmindex, node low-voltage risk R _lVmthe computing formula of index is:

In formula (15), R _lVmit is the node low-voltage risk after element m fault under wind and rain weather conditions is stopped transport; G ₂it is electric system node low-voltage accident condition collection under wind and rain weather conditions; N is node sum; V _iactual node voltage during line fault under wind and rain weather conditions; V ₀the node voltage of circuit while normally moving.

7. according to a kind of described in claim 5 or 6, be applicable to electrical network under wind and rain weather conditions and lose load methods of risk assessment, it is characterized in that, the optimum that optimum Nonlinear programming Model adopts the nonlinear solver (fmincon) based on matlab optimization tool bag to calculate evaluated electrical network loses load, and the calculating implementation procedure that this optimum loses load is:

(7-1) select fmincon to process the form of nonlinear programming problem

X＝fmincon(FUN，XO，A，B，Aeq，Beq，LB，UB，NONLCON)；

(7-2) set unknown vector X, the subvector using the unknown quantity in optimum Nonlinear programming Model as X, and compose with initial value;

(7-3) set the bound of unknown vector X;

(7-4) write and take minimum and lose the subfunction FUN that load is objective function;

(7-5) write optimum Nonlinear programming Model and process subfunction NONLCON, the value of the vectorial X that each iteration is obtained is carried out iterative;

(7-6) fmincon solves the minimum load of losing after calling subfunction FUN, NONLCON, and this minimum is lost the optimum mistake load that load is evaluated electrical network under wind and rain weather conditions.